Overview

Dataset statistics

Number of variables15
Number of observations177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.9 KiB
Average record size in memory120.7 B

Variable types

Numeric14
Categorical1

Alerts

Unnamed: 0 is highly overall correlated with Alcalinity of ash and 6 other fieldsHigh correlation
Alcohol is highly overall correlated with Color intensity and 2 other fieldsHigh correlation
Malic acid is highly overall correlated with HueHigh correlation
Alcalinity of ash is highly overall correlated with Unnamed: 0High correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Total phenols is highly overall correlated with Unnamed: 0 and 4 other fieldsHigh correlation
Flavanoids is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Nonflavanoid phenols is highly overall correlated with FlavanoidsHigh correlation
Proanthocyanins is highly overall correlated with Total phenols and 2 other fieldsHigh correlation
Color intensity is highly overall correlated with Alcohol and 1 other fieldsHigh correlation
Hue is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
OD280/OD315 of diluted wines is highly overall correlated with Unnamed: 0 and 4 other fieldsHigh correlation
Proline is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
Class is highly overall correlated with Unnamed: 0 and 7 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-11-20 03:45:12.817673
Analysis finished2023-11-20 03:45:35.194114
Duration22.38 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct177
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88
Minimum0
Maximum176
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:35.287355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q144
median88
Q3132
95-th percentile167.2
Maximum176
Range176
Interquartile range (IQR)88

Descriptive statistics

Standard deviation51.239633
Coefficient of variation (CV)0.58226856
Kurtosis-1.2
Mean88
Median Absolute Deviation (MAD)44
Skewness0
Sum15576
Variance2625.5
MonotonicityStrictly increasing
2023-11-20T03:45:35.452052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 1
 
0.6%
161 1
 
0.6%
162 1
 
0.6%
163 1
 
0.6%
164 1
 
0.6%
165 1
 
0.6%
166 1
 
0.6%
167 1
 
0.6%
168 1
 
0.6%
9 1
 
0.6%
Other values (167) 167
94.4%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%

Class
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
71 
1
58 
3
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters177
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Length

2023-11-20T03:45:35.578937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T03:45:35.680837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring characters

ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 71
40.1%
1 58
32.8%
3 48
27.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.993672
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:35.804049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.658
Q112.36
median13.05
Q313.67
95-th percentile14.22
Maximum14.83
Range3.8
Interquartile range (IQR)1.31

Descriptive statistics

Standard deviation0.80880844
Coefficient of variation (CV)0.062246332
Kurtosis-0.84014629
Mean12.993672
Median Absolute Deviation (MAD)0.68
Skewness-0.046483486
Sum2299.88
Variance0.6541711
MonotonicityNot monotonic
2023-11-20T03:45:35.998011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.37 6
 
3.4%
13.05 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.3%
12.25 3
 
1.7%
12 3
 
1.7%
12.42 3
 
1.7%
13.4 2
 
1.1%
14.38 2
 
1.1%
14.1 2
 
1.1%
Other values (115) 141
79.7%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.22 2
1.1%
14.21 1
0.6%
14.2 1
0.6%

Malic acid
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.339887
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:36.153356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.058
Q11.6
median1.87
Q33.1
95-th percentile4.464
Maximum5.8
Range5.06
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1193144
Coefficient of variation (CV)0.47836259
Kurtosis0.27858088
Mean2.339887
Median Absolute Deviation (MAD)0.52
Skewness1.0309746
Sum414.16
Variance1.2528648
MonotonicityNot monotonic
2023-11-20T03:45:36.288403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
4.0%
1.81 4
 
2.3%
1.67 4
 
2.3%
1.9 3
 
1.7%
1.51 3
 
1.7%
1.61 3
 
1.7%
1.68 3
 
1.7%
1.53 3
 
1.7%
1.35 3
 
1.7%
2.05 2
 
1.1%
Other values (123) 142
80.2%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash
Real number (ℝ)

Distinct78
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3661582
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:36.416167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.56
95-th percentile2.742
Maximum3.23
Range1.87
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.27508044
Coefficient of variation (CV)0.11625615
Kurtosis1.1223747
Mean2.3661582
Median Absolute Deviation (MAD)0.16
Skewness-0.17240561
Sum418.81
Variance0.075669248
MonotonicityNot monotonic
2023-11-20T03:45:36.555819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 7
 
4.0%
2.3 7
 
4.0%
2.7 6
 
3.4%
2.32 6
 
3.4%
2.36 6
 
3.4%
2.48 5
 
2.8%
2.38 5
 
2.8%
2.2 5
 
2.8%
2.1 4
 
2.3%
2.5 4
 
2.3%
Other values (68) 122
68.9%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Alcalinity of ash
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.516949
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:36.709694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.76
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3360711
Coefficient of variation (CV)0.170932
Kurtosis0.50667253
Mean19.516949
Median Absolute Deviation (MAD)2
Skewness0.20407561
Sum3454.5
Variance11.12937
MonotonicityNot monotonic
2023-11-20T03:45:36.860794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.5%
21 11
 
6.2%
16 11
 
6.2%
18 10
 
5.6%
19 9
 
5.1%
21.5 8
 
4.5%
19.5 7
 
4.0%
18.5 7
 
4.0%
22 7
 
4.0%
22.5 7
 
4.0%
Other values (52) 85
48.0%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.587571
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:37.010524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.8
Q188
median98
Q3107
95-th percentile123.2
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.174018
Coefficient of variation (CV)0.14232718
Kurtosis2.2643344
Mean99.587571
Median Absolute Deviation (MAD)10
Skewness1.1221477
Sum17627
Variance200.9028
MonotonicityNot monotonic
2023-11-20T03:45:37.160242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.2%
101 9
 
5.1%
98 9
 
5.1%
96 8
 
4.5%
102 7
 
4.0%
112 6
 
3.4%
85 6
 
3.4%
94 6
 
3.4%
89 5
 
2.8%
Other values (42) 97
54.8%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.2%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
126 1
0.6%
124 1
0.6%
123 1
0.6%

Total phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2922599
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:37.290019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.74
median2.35
Q32.8
95-th percentile3.276
Maximum3.88
Range2.9
Interquartile range (IQR)1.06

Descriptive statistics

Standard deviation0.62646508
Coefficient of variation (CV)0.27329584
Kurtosis-0.83241828
Mean2.2922599
Median Absolute Deviation (MAD)0.51
Skewness0.097688265
Sum405.73
Variance0.3924585
MonotonicityNot monotonic
2023-11-20T03:45:37.425498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
3 6
 
3.4%
2.6 6
 
3.4%
2.95 5
 
2.8%
2.8 5
 
2.8%
2 5
 
2.8%
2.45 4
 
2.3%
2.85 4
 
2.3%
1.65 4
 
2.3%
1.38 4
 
2.3%
Other values (87) 126
71.2%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.3%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0234463
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:37.678163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.544
Q11.2
median2.13
Q32.86
95-th percentile3.5
Maximum5.08
Range4.74
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation0.99865762
Coefficient of variation (CV)0.49354292
Kurtosis-0.87216456
Mean2.0234463
Median Absolute Deviation (MAD)0.83
Skewness0.036879791
Sum358.15
Variance0.99731703
MonotonicityNot monotonic
2023-11-20T03:45:37.811062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.3%
1.25 3
 
1.7%
2.68 3
 
1.7%
0.6 3
 
1.7%
2.03 3
 
1.7%
0.58 3
 
1.7%
0.7 2
 
1.1%
3.15 2
 
1.1%
2.98 2
 
1.1%
3.39 2
 
1.1%
Other values (121) 150
84.7%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Nonflavanoid phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36231638
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:37.941867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.44
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.12465293
Coefficient of variation (CV)0.34404443
Kurtosis-0.64669127
Mean0.36231638
Median Absolute Deviation (MAD)0.09
Skewness0.44093698
Sum64.13
Variance0.015538354
MonotonicityNot monotonic
2023-11-20T03:45:38.085533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.26 11
 
6.2%
0.43 11
 
6.2%
0.29 10
 
5.6%
0.32 9
 
5.1%
0.27 8
 
4.5%
0.34 8
 
4.5%
0.3 8
 
4.5%
0.37 8
 
4.5%
0.4 8
 
4.5%
0.24 7
 
4.0%
Other values (29) 89
50.3%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
4.0%
0.25 2
 
1.1%
0.26 11
6.2%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.3%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
4.0%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5869492
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:38.211930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.55
Q31.95
95-th percentile2.712
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57154472
Coefficient of variation (CV)0.36015314
Kurtosis0.59786217
Mean1.5869492
Median Absolute Deviation (MAD)0.37
Skewness0.53278674
Sum280.89
Variance0.32666337
MonotonicityNot monotonic
2023-11-20T03:45:38.346584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.1%
1.46 7
 
4.0%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.56 4
 
2.3%
1.66 4
 
2.3%
1.98 4
 
2.3%
2.08 4
 
2.3%
1.95 3
 
1.7%
1.62 3
 
1.7%
Other values (91) 128
72.3%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0548023
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:38.509024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.112
Q13.21
median4.68
Q36.2
95-th percentile9.604
Maximum13
Range11.72
Interquartile range (IQR)2.99

Descriptive statistics

Standard deviation2.3244464
Coefficient of variation (CV)0.45984913
Kurtosis0.36993779
Mean5.0548023
Median Absolute Deviation (MAD)1.52
Skewness0.87085005
Sum894.7
Variance5.4030512
MonotonicityNot monotonic
2023-11-20T03:45:38.664281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8 4
 
2.3%
2.6 4
 
2.3%
4.6 4
 
2.3%
4.5 3
 
1.7%
5.4 3
 
1.7%
5.6 3
 
1.7%
2.9 3
 
1.7%
5.7 3
 
1.7%
3.05 3
 
1.7%
3.4 3
 
1.7%
Other values (121) 144
81.4%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95698305
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:38.815114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.78
median0.96
Q31.12
95-th percentile1.286
Maximum1.71
Range1.23
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.22913505
Coefficient of variation (CV)0.2394348
Kurtosis-0.35507479
Mean0.95698305
Median Absolute Deviation (MAD)0.16
Skewness0.026963707
Sum169.386
Variance0.052502869
MonotonicityNot monotonic
2023-11-20T03:45:38.997766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.23 7
 
4.0%
1.04 7
 
4.0%
1.12 6
 
3.4%
0.96 5
 
2.8%
1.25 5
 
2.8%
0.57 5
 
2.8%
0.89 5
 
2.8%
1.07 4
 
2.3%
1.05 4
 
2.3%
0.86 4
 
2.3%
Other values (68) 125
70.6%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

OD280/OD315 of diluted wines
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6042938
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:39.131540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.46
Q11.93
median2.78
Q33.17
95-th percentile3.572
Maximum4
Range2.73
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation0.7051029
Coefficient of variation (CV)0.2707463
Kurtosis-1.1039183
Mean2.6042938
Median Absolute Deviation (MAD)0.52
Skewness-0.32042445
Sum460.96
Variance0.4971701
MonotonicityNot monotonic
2023-11-20T03:45:39.259104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
2.78 4
 
2.3%
3 4
 
2.3%
1.82 4
 
2.3%
2.77 3
 
1.7%
1.56 3
 
1.7%
1.33 3
 
1.7%
3.17 3
 
1.7%
1.75 3
 
1.7%
2.31 3
 
1.7%
Other values (111) 142
80.2%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%
3.56 1
0.6%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean745.09605
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-20T03:45:39.387281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.4
Q1500
median672
Q3985
95-th percentile1298
Maximum1680
Range1402
Interquartile range (IQR)485

Descriptive statistics

Standard deviation314.88405
Coefficient of variation (CV)0.42260867
Kurtosis-0.2194105
Mean745.09605
Median Absolute Deviation (MAD)200
Skewness0.78379985
Sum131882
Variance99151.962
MonotonicityNot monotonic
2023-11-20T03:45:39.528076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 5
 
2.8%
680 5
 
2.8%
625 4
 
2.3%
750 4
 
2.3%
630 4
 
2.3%
495 3
 
1.7%
480 3
 
1.7%
660 3
 
1.7%
510 3
 
1.7%
450 3
 
1.7%
Other values (111) 140
79.1%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Interactions

2023-11-20T03:45:33.363091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.155598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.737543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.231645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.794363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.174214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.719622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.028619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.298270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.663063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.939386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.256972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.593301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.947675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.480387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.314146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.844253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.321871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.889702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.272791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.804821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.131168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.386636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.756117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.032853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.353671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.684624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.040354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.598625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.461473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.074546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.416156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.987142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.377200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.895619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.231800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.473529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.849415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.131018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.450208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.906955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.148035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.689848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.576278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.171142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.505997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.091970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.468944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.976227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.316543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.552520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.929800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.217099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.531915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.985826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.258403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.792935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.671771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.268466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.597113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.221225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.595556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.083643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.408999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.637974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.022419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.314026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.641156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.090239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.356879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.888184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.773598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.371374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.970297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.325525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.723088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.215094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.522729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.744722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.119885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.409971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.743267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.182244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.452397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.970343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.856141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.458766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.051292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.414888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.818396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.292472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.601475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.841128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.198267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.517667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.843204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.263597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.534170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.060786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:15.952263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.558942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.144258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.514519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.915399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.380053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.687909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.950451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.282743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.609203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.954733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.352558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.647434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.150953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.042557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.643018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.224916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.599733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.014664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.477231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.768190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.027048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.377544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.694747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.036341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.431745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.741498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.241529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.137837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.732934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.305905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.682238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.110180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.572969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.849743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.104339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.461707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.780721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.120202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.511741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.849142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.336987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.260285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.825888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.397376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.810737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.229139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.668401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:24.936800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.228634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.572350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.868055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.214892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.609633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:32.967460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.424392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.371161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:17.916844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.479589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.896420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.322366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.755228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.037963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.310313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.657995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:28.956035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.299881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.693300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.072101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.505929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.495473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.009798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.581897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:20.990020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.414962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.842549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.121338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.390178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.740495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.045640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.401551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.774387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.160384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:34.603610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:16.612008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:18.130037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:19.685228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:21.083746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:22.515000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:23.937852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:25.208598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:26.471277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:27.828054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:29.132127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:30.489489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:31.860297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T03:45:33.249311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-20T03:45:39.644774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Unnamed: 0AlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProlineClass
Unnamed: 01.000-0.3510.423-0.0250.604-0.248-0.655-0.7690.477-0.4720.136-0.685-0.658-0.5750.917
Alcohol-0.3511.0000.1460.242-0.2970.3570.3050.286-0.1580.1830.636-0.0280.0890.6300.579
Malic acid0.4230.1461.0000.2330.3040.085-0.280-0.3250.255-0.2430.292-0.561-0.254-0.0550.496
Ash-0.0250.2420.2331.0000.3730.3610.1300.0760.1480.0220.283-0.052-0.0120.2510.222
Alcalinity of ash0.604-0.2970.3040.3731.000-0.159-0.372-0.4380.387-0.245-0.071-0.352-0.317-0.4510.378
Magnesium-0.2480.3570.0850.361-0.1591.0000.2400.225-0.2330.1630.3550.0330.0420.5040.399
Total phenols-0.6550.305-0.2800.130-0.3720.2401.0000.879-0.4470.6660.0070.4400.6870.4150.558
Flavanoids-0.7690.286-0.3250.076-0.4380.2250.8791.000-0.5440.729-0.0500.5370.7400.4230.751
Nonflavanoid phenols0.477-0.1580.2550.1480.387-0.233-0.447-0.5441.000-0.3830.062-0.268-0.494-0.2670.353
Proanthocyanins-0.4720.183-0.2430.022-0.2450.1630.6660.729-0.3831.000-0.0370.3420.5490.3020.395
Color intensity0.1360.6360.2920.283-0.0710.3550.007-0.0500.062-0.0371.000-0.421-0.3260.4560.648
Hue-0.685-0.028-0.561-0.052-0.3520.0330.4400.537-0.2680.342-0.4211.0000.4870.2050.581
OD280/OD315 of diluted wines-0.6580.089-0.254-0.012-0.3170.0420.6870.740-0.4940.549-0.3260.4871.0000.2450.642
Proline-0.5750.630-0.0550.251-0.4510.5040.4150.423-0.2670.3020.4560.2050.2451.0000.643
Class0.9170.5790.4960.2220.3780.3990.5580.7510.3530.3950.6480.5810.6420.6431.000

Missing values

2023-11-20T03:45:34.758533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T03:45:35.101106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
00113.201.782.1411.21002.652.760.261.284.381.053.401050
11113.162.362.6718.61012.803.240.302.815.681.033.171185
22114.371.952.5016.81133.853.490.242.187.800.863.451480
33113.242.592.8721.01182.802.690.391.824.321.042.93735
44114.201.762.4515.21123.273.390.341.976.751.052.851450
55114.391.872.4514.6962.502.520.301.985.251.023.581290
66114.062.152.6117.61212.602.510.311.255.051.063.581295
77114.831.642.1714.0972.802.980.291.985.201.082.851045
88113.861.352.2716.0982.983.150.221.857.221.013.551045
99114.102.162.3018.01052.953.320.222.385.751.253.171510
Unnamed: 0ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
167167313.582.582.6924.51051.550.840.391.548.6600000.741.80750
168168313.404.602.8625.01121.980.960.271.118.5000000.671.92630
169169312.203.032.3219.0961.250.490.400.735.5000000.661.83510
170170312.772.392.2819.5861.390.510.480.649.8999990.571.63470
171171314.162.512.4820.0911.680.700.441.249.7000000.621.71660
172172313.715.652.4520.5951.680.610.521.067.7000000.641.74740
173173313.403.912.4823.01021.800.750.431.417.3000000.701.56750
174174313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
175175313.172.592.3720.01201.650.680.531.469.3000000.601.62840
176176314.134.102.7424.5962.050.760.561.359.2000000.611.60560